{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchtext\n",
    "import numpy as np\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from torchtext.vocab import Vectors\n",
    "from tqdm import tqdm_notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = torchtext.data.Field(include_lengths = False)\n",
    "label = torchtext.data.Field(sequential=False)\n",
    "train, val, test = torchtext.datasets.SST.splits(text, label, filter_pred=lambda ex: ex.label != 'neutral')\n",
    "text.build_vocab(train)\n",
    "label.build_vocab(train)\n",
    "train_iter, val_iter, test_iter = torchtext.data.BucketIterator.splits((train, val, test), batch_size=10, device=-1, repeat = False)\n",
    "url = 'https://s3-us-west-1.amazonaws.com/fasttext-vectors/wiki.simple.vec'\n",
    "text.vocab.load_vectors(vectors=Vectors('wiki.simple.vec', url=url))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch = next(iter(train_iter))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
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       "[torch.LongTensor of size 12]"
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch.text[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
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      "[torch.FloatTensor of size 300]\n",
      "\n",
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      "[torch.FloatTensor of size 300]\n",
      "\n",
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      "[torch.FloatTensor of size 300]\n",
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      "[torch.FloatTensor of size 300]\n",
      "\n",
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      "[torch.FloatTensor of size 300]\n",
      "\n",
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      "[torch.FloatTensor of size 300]\n",
      "\n",
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      "[torch.FloatTensor of size 300]\n",
      "\n",
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      "[torch.FloatTensor of size 300]\n",
      "\n",
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      "[torch.FloatTensor of size 300]\n",
      "\n",
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      "[torch.FloatTensor of size 300]\n",
      "\n",
      "\n",
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      "[torch.FloatTensor of size 300]\n",
      "\n",
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      "\n",
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      "[torch.FloatTensor of size 300]\n",
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      "[torch.FloatTensor of size 300]\n",
      "\n",
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      "[torch.FloatTensor of size 300]\n",
      "\n",
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      "-0.1949\n",
      "-0.0695\n",
      "-0.1606\n",
      "-0.0989\n",
      "[torch.FloatTensor of size 300]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in batch.text[:, 0]:\n",
    "    print(text.vocab.vectors[i.data[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "-0.0274\n",
       " 0.3831\n",
       "-0.1461\n",
       "-0.0578\n",
       " 0.1725\n",
       "-0.0329\n",
       " 0.1526\n",
       "-0.3234\n",
       "-0.0042\n",
       "-0.1760\n",
       " 0.3564\n",
       "-0.0464\n",
       "-0.2248\n",
       "-0.0251\n",
       "-0.3221\n",
       " 0.0133\n",
       "-0.3362\n",
       "-0.2645\n",
       "-0.0559\n",
       " 0.0390\n",
       " 0.0054\n",
       "-0.2019\n",
       "-0.0112\n",
       "-0.0305\n",
       "-0.2582\n",
       "[torch.FloatTensor of size 300]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text.vocab.vectors[text.vocab.stoi['follows']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NaiveBayes:\n",
    "    def __init__(self, text):\n",
    "        self.train_iter = train_iter\n",
    "        self.test_iter = test_iter\n",
    "        self.val_iter = val_iter\n",
    "        self.array_like = np.zeros((train_iter.batch_size, len(text.vocab)))\n",
    "        \n",
    "    def binarize_occurrences(self, indices):\n",
    "        occurrences = self.array_like.copy()\n",
    "        for idx, entry in enumerate(indices): occurrences[idx][entry] = 1\n",
    "        return occurrences\n",
    "\n",
    "    def batch_to_input(self, batch, train = True):\n",
    "        word_indices = batch.text.data.numpy().T\n",
    "        x = self.binarize_occurrences(word_indices)\n",
    "        if train:\n",
    "            y = batch.label.data.numpy()\n",
    "            return x, y\n",
    "        else:\n",
    "            return x\n",
    "\n",
    "    def train_mnb(self, train_iter, val_iter, no_epochs):\n",
    "        self.model = MultinomialNB(alpha=1.0, fit_prior=True)\n",
    "        for epoch in tqdm_notebook(range(1, no_epochs+1)):\n",
    "            for batch in train_iter:\n",
    "                x, y = self.batch_to_input(batch, train = True)\n",
    "                self.model.partial_fit(x, y, classes = [1,2])\n",
    "            \n",
    "            if epoch % 1 == 0:\n",
    "                acc = self.validate(val_iter)\n",
    "                print('Epoch ', epoch, '| Validation Accuracy: ', acc)\n",
    "        print('Done training.')\n",
    "        \n",
    "    def test(self, test_iter):\n",
    "        \"All models should be able to be run with following command.\"\n",
    "        upload, trues = [], []\n",
    "\n",
    "        for batch in test_iter:\n",
    "            x, y = self.batch_to_input(batch, train = False), batch.label\n",
    "            probs = self.model.predict(x)\n",
    "            upload += list(probs)\n",
    "            trues += list(y.data)\n",
    "        correct = sum([1 if i == j else 0 for i, j in zip(upload, trues)])\n",
    "        accuracy = correct / len(trues)\n",
    "        print('Test Accuracy: ', accuracy)\n",
    "        \n",
    "        with open(\"predictions.txt\", \"w\") as f:\n",
    "            for u in upload:\n",
    "                f.write(str(u) + \"\\n\")\n",
    "                \n",
    "    def validate(self, val_iter):\n",
    "        y_p, y_t, correct = [], [], 0\n",
    "        for batch in val_iter:\n",
    "            x, y = self.batch_to_input(batch, train = False), batch.label\n",
    "            probs = self.model.predict(x)[:len(y.data)]\n",
    "            y_p += list(probs)\n",
    "            y_t += list(y.data)\n",
    "        correct = sum([1 if i == j else 0 for i, j in zip(y_p, y_t)])\n",
    "        accuracy = correct / len(y_p)\n",
    "        return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f1036e3f65d1442a8766029a471d3649",
       "version_major": 2,
       "version_minor": 0
      },
      "text/html": [
       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
       "<p>\n",
       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
       "  that the widgets JavaScript is still loading. If this message persists, it\n",
       "  likely means that the widgets JavaScript library is either not installed or\n",
       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
       "  Widgets Documentation</a> for setup instructions.\n",
       "</p>\n",
       "<p>\n",
       "  If you're reading this message in another frontend (for example, a static\n",
       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
       "  it may mean that your frontend doesn't currently support widgets.\n",
       "</p>\n"
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      "text/plain": [
       "HBox(children=(IntProgress(value=0, max=1), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch  1 | Validation Accuracy:  0.7981651376146789\n",
      "\n",
      "Done training.\n",
      "Test Accuracy:  0.8209774848984075\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sob/Desktop/cs287/homeworks/env/lib/python3.6/site-packages/ipykernel_launcher.py:25: DeprecationWarning: generator 'Iterator.__iter__' raised StopIteration\n",
      "/Users/sob/Desktop/cs287/homeworks/env/lib/python3.6/site-packages/ipykernel_launcher.py:53: DeprecationWarning: generator 'Iterator.__iter__' raised StopIteration\n",
      "/Users/sob/Desktop/cs287/homeworks/env/lib/python3.6/site-packages/ipykernel_launcher.py:38: DeprecationWarning: generator 'Iterator.__iter__' raised StopIteration\n"
     ]
    }
   ],
   "source": [
    "mnb = NaiveBayes(text)\n",
    "mnb.train_mnb(train_iter, val_iter, 1)\n",
    "mnb.test(test_iter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__iter__',\n",
       " '__le__',\n",
       " '__len__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " '_iterations_this_epoch',\n",
       " '_random_state_this_epoch',\n",
       " '_restored_from_state',\n",
       " 'batch_size',\n",
       " 'batch_size_fn',\n",
       " 'batches',\n",
       " 'create_batches',\n",
       " 'data',\n",
       " 'dataset',\n",
       " 'device',\n",
       " 'epoch',\n",
       " 'init_epoch',\n",
       " 'iterations',\n",
       " 'load_state_dict',\n",
       " 'random_shuffler',\n",
       " 'repeat',\n",
       " 'shuffle',\n",
       " 'sort',\n",
       " 'sort_key',\n",
       " 'sort_within_batch',\n",
       " 'splits',\n",
       " 'state_dict',\n",
       " 'train']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(train_iter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<torchtext.data.iterator.BucketIterator at 0x1142df048>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_iter"
   ]
  }
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